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Record W4417107450 · doi:10.1111/rode.70097

Food Gifting and Household Food Security

2025· article· en· W4417107450 on OpenAlexafffund
Shaoyan Sun, Henry An, Philippe Marcoul

Bibliographic record

VenueReview of Development Economics · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsUniversity of Alberta
FundersInternational Development Research Centre
KeywordsFood securityIncentiveConstruct (python library)AgricultureCompensation (psychology)Nonmarket forcesAgrarian societyFood processingDeveloping country

Abstract

fetched live from OpenAlex

ABSTRACT Food gifting provides an important means of risk sharing in agrarian economies where farming households have limited access to formal credit and insurance markets. Food gifting is also an important source of food for households that are struggling with food scarcity. However, gifting may lead to free‐riding behavior and reduce the incentive for adopting risk‐reducing production practices. We construct a simple conceptual model of gifting to show that being in a food gifting regime reduces the adoption of risk‐reducing practices. Using primary data from rural Tanzania, we then test the prediction of our conceptual model. We estimate an endogenous switching model and find that engaging in food gifting is correlated with a reduction in the adoption of risk‐reducing practices. We find no evidence, however, that food gifting has a statistically significant relationship with household food security. Our findings suggest that informal nonmarket risk‐sharing institutions in the global South may not be sufficient on their own to address food security.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.203
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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